Watershed Hydrologic Modeling and Sediment and Nutrient ......of this effort, a comprehensive...

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Watershed Hydrologic Modeling and Sediment and Nutrient Loading Estimation for the Lake Tahoe Total Maximum Daily Load October, 2005 Revised: February 2007 Prepared for: Lahontan Regional Water Quality Control Board 2501 Lake Tahoe Blvd. South Lake Tahoe, CA 96150 And University of California–Davis 1441 Research Park Drive #171 Davis, CA 95616 Prepared by: Tetra Tech, Inc. 10306 Eaton Place, Suite 340 Fairfax, VA 22030

Transcript of Watershed Hydrologic Modeling and Sediment and Nutrient ......of this effort, a comprehensive...

  • Watershed Hydrologic Modeling and Sediment and Nutrient Loading

    Estimation for the Lake Tahoe Total Maximum Daily Load

    October, 2005

    Revised: February 2007

    Prepared for:

    Lahontan Regional Water Quality Control Board 2501 Lake Tahoe Blvd.

    South Lake Tahoe, CA 96150

    And

    University of California–Davis 1441 Research Park Drive #171

    Davis, CA 95616

    Prepared by:

    Tetra Tech, Inc. 10306 Eaton Place, Suite 340

    Fairfax, VA 22030

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    Contents

    1. PROJECT DEFINITION .................................................................................................................. 1

    2. MODEL SELECTION ........................................................................................................................ 4

    2.1. SELECTION CRITERIA ................................................................................................................... 5 2.2. LOADING SIMULATION PROGRAM C++ (LSPC) OVERVIEW......................................................... 6

    3. MODELING APPROACH................................................................................................................. 8

    3.1. WATERSHED SEGMENTATION ......................................................................................................8 3.2. WATER BODY REPRESENTATION................................................................................................. 9 3.3. METEOROLOGICAL DATA ........................................................................................................... 10

    Local Weather Data .......................................................................................................................... 11 Local Temperature Data .................................................................................................................. 13 Lapse Rate Calculations ................................................................................................................... 14 Evapotranspiration Calculations......................................................................................................15 Synthetic Weather Dataset.............................................................................................................. 16

    3.4. LAND USE REPRESENTATION..................................................................................................... 24 The Parcel Boundaries Layer .......................................................................................................... 27 Hard Impervious Cover Layer ......................................................................................................... 27 Upland Erosion Potential.................................................................................................................. 28 Land Use Categorization/Reclassification ...................................................................................... 28 GIS Layering Process ....................................................................................................................... 32

    3.5. SOILS ......................................................................................................................................... 37

    4. MODEL CALIBRATION AND VALIDATION............................................................................ 42

    4.1. HYDROLOGY CALIBRATION ....................................................................................................... 43 Snow Hydrology Simulation ............................................................................................................ 44 Hydrology Simulation ....................................................................................................................... 47

    4.2. WATER QUALITY CALIBRATION ................................................................................................ 51 Estimating Sediment Loads with Log-Transform Regression ..................................................... 52 Pollutant Export Analysis Using Regression and Hydrograph Separation ................................ 55 Estimating Seasonal Pollutant Loading Patterns in the Streams ............................................... 56 Model Parameterization by Land Use ............................................................................................ 60

    5. DISCUSSION OF MODEL RESULTS ......................................................................................... 68

    6. EXPLORATORY SCENARIO: POTENTIAL IMPACTS ASSOCIATED WITH CLIMATE CHANGE ..................................................................................................................................................... 71

    Development of Climate Change Projections ............................................................................... 71 Watershed Model Results - Hydrology .......................................................................................... 75

    7. REFERENCES .................................................................................................................................. 79

    APPENDIX A: MODEL CALIBRATION RESULTS ........................................................................... 82

    APPENDIX B: LAKE TAHOE WATERSHED MODEL LAND USE RUNOFF

    PARAMETERIZATION............................................................................................................................ 83

    APPENDIX C: WATERSHED MODEL RESULTS ............................................................................. 86

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    FIGURES FIGURE 1-1. LOCATION OF THE LAKE TAHOE BASIN. ...................................................................................... 2 FIGURE 2-1. CONCEPTUAL DATA FLOW AND INTERACTIONS FOR A WATERSHED MODEL. ................................ 4 FIGURE 3-1. SUBWATERSHED DELINEATION AND ELEVATION IN THE LAKE TAHOE BASIN.............................. 9 FIGURE 3-2. STREAM CHANNEL REPRESENTATION IN THE LSPC MODEL. ...................................................... 10 FIGURE 3-3. LOCATION OF SNOTEL AND NCDC WEATHER STATIONS IN THE LAKE TAHOE........................ 12 BASIN............................................................................................................................................................ 12 FIGURE 3-4. ORIGINAL VS. CORRECTED SNOTEL TEMPERATURE TIME SERIES AT FALLEN LEAF LAKE. ...... 13 FIGURE 3-5. SCATTER PLOTS OF SNOTEL TEMPERATURE VS. ELEVATION FOR REGIONAL LAPSE RATE

    ESTIMATE............................................................................................................................................. 14 FIGURE 3-6. MONTHLY MODELED EVAPOTRANSPIRATION (ET) AT WARD CREEK VS. OBSERVED ET AT

    TAHOE CITY . ....................................................................................................................................... 16 FIGURE 3-7. LOCATION OF THE 142 MM5 WEATHER GRID CELLS IN THE LAKE TAHOE BASIN. ..................... 19 FIGURE 3-8. LOCATION OF SNOTEL GAGES RELATIVE TO SELECTED MM5 CELLS WITH.............................. 20 COMPARABLE ELEVATION. ............................................................................................................................ 20 FIGURE 3-9. MM5 VS. OBSERVED SNOTEL ELEVATION AND TEMPERATURE. .............................................. 21 FIGURE 3-10. PREDICTED MM5 TEMPERATURE VS. OBSERVED SNOTEL TEMPERATURE AND ELEVATION... 22 FIGURE 3-11. PREDICTED MM5 PRECIPITATION VS. OBSERVED SNOTEL PRECIPITATION AND ELEVATION.. 23 FIGURE 3-12. SEASONAL MM5 PRECIPITATION VS. OBSERVED SNOTEL PRECIPITATION AT WARD CREEK. 23 FIGURE 3-13. FINAL COMPOSITE LAND USE COVERAGE FOR THE LAKE TAHOE BASIN. ................................. 26 FIGURE 3-14. HARD IMPERVIOUS COVER FOR THE LAKE TAHOE BASIN AND EXAMPLE FOCUS AREA. ........... 28 FIGURE 3-15. EXAMPLE OF PARCEL REFINEMENTS IN A PORTION OF THE HEAVENLY SKI AREA. .................. 30 FIGURE 3-16. EXAMPLE OF PARCEL REFINEMENT IN A CAMPGROUND PARCEL BOUNDARY NEAR EMERALD

    BAY ON THE SOUTHWESTERN SHORE OF LAKE TAHOE. ....................................................................... 31 FIGURE 3-17. EXAMPLE OF PARCEL REFINEMENT FOR HIGHWAY RIGHT-OF-WAY OWNERSHIP (IMAGE ON LEFT)

    TO ACTUAL HIGHWAY WIDTHS BASED ON HARD-COVER IMPERVIOUS OVERLAY (IMAGE ON RIGHT)..... 32 FIGURE 3-18. FOREST FIRE BOUNDARIES SHADED WITH BURN SEVERITY FOR THE GONDOLA FIRE. (THE

    RIGHT PANEL SHOWS HOW THE AFFECTED AREAS ARE AGGREGATED BY SUBWATERSHED.)................. 35 FIGURE 3-19. MAP OF UPLAND EROSION POTENTIAL FOR THE LAKE TAHOE BASIN....................................... 36 FIGURE 3-20. SSURGO MAP UNITS AND CORRESPONDING SOIL DESCRIPTIONS. ........................................... 38 FIGURE 3-21. AVERAGE PERMEABILITY OF LAKE TAHOE BASIN SOILS. ........................................................ 40 FIGURE 3-22. USLE K EROSION FACTOR FOR SURFACE SOILS. ...................................................................... 41 FIGURE 4-1. HYDROLOGY AND WATER QUALITY CALIBRATION LOCATIONS.................................................. 43 FIGURE 4-3. MODELED VS. OBSERVED DAILY AVERAGE TEMPERATURES AND SNOW WATER EQUIVALENT

    DEPTHS AT WARD CREEK SNOTEL SITE (OCTOBER 1996–DECEMBER 2004)..................................... 46 FIGURE 4-4. HYDROLOGY CALIBRATION FOR WARD CREEK WITH EMPHASIS ON WATER YEAR.................... 49 1997. 49 FIGURE 4-5. HYDROLOGY VALIDATION FOR WARD CREEK WITH SEASONAL MEAN, MEDIAN, AND VARIATION .

    ............................................................................................................................................................ 49 FIGURE 4-6. SAMPLE FLOW DISTRIBUTIONS FOR SEDIMENT OBSERVATIONS AT WARD CREEK...................... 53 FIGURE 4-7. FLOW-SEDIMENT YIELD REGRESSION RELATIONSHIP AND ERROR DISTRIBUTION AT WARD

    CREEK. ................................................................................................................................................ 54 FIGURE 4-8. HYDROGRAPH SEPARATION FOR WARD CREEK (USGS 10336676) USING HISTORICAL FLOW

    DATA COLLECTED BETWEEN 10/1/1972 AND 9/30/2003....................................................................... 57 FIGURE 4-9. SEASONAL NITROGEN AND PHOSPHORUS CONSTITUENT DISTRIBUTION FOR WARD CREEK WATER

    QUALITY SAMPLING DATA COLLECTED BETWEEN 1972 AND 2003, DERIVED FROM HYDROGRAPH SEPARATION AND REGRESSION. ........................................................................................................... 59

    FIGURE 4-10. LSPC MODEL RESULTS VS. OBSERVED DATA FOR TSS AT WARD CREEK. ............................... 66 FIGURE 4-11. LSPC MODEL RESULTS VS. OBSERVED DATA FOR TN AT WARD CREEK. ................................. 66 FIGURE 4-12. LSPC MODEL RESULTS VS. OBSERVED DATA FOR TP AT WARD CREEK................................... 66 FIGURE 4-13. LSPC MODEL RESULTS VS. OBSERVED DATA FOR DN AT WARD CREEK. ................................ 67 FIGURE 4-14. LSPC MODEL RESULTS VS. OBSERVED DATA FOR DP AT WARD CREEK. ................................. 67

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    TABLES TABLE 3-1. WEATHER STATIONS AND ASSOCIATED DATA USED TO SIMULATE WEATHER.............................. 11 CONDITIONS .................................................................................................................................................. 11 TABLE 3-2. SNOTEL GAGES AND SUMMARY INFORMATION (OCTOBER 1990- SEPTEMBER......................... 21 2000)............................................................................................................................................................. 21 TABLE 3-3. AVERAGE PERCENTAGE OF DIFFERENCE BETWEEN SNOTEL AND MM5 TEMPERATURES DURING

    WINTER SEASON................................................................................................................................... 22 TABLE 3-4. YEARLY PERCENTAGE OF DIFFERENCE BETWEEN SNOTEL AND MM5 TOTAL PRECIPITATION

    DURING WINTER SEASON...................................................................................................................... 24 TABLE 3-5. MODELING LAND USE CATEGORIES DERIVED FROM THE COMPOSITE LAND USE LAYER............... 33 TABLE 3-6. PERCENT COVERAGE FOR EACH OF THE FIVE VEGETATED-UNIMPACTED CATEGORIES............... 37 (BASED ON EROSION POTENTIAL)................................................................................................................... 37 TABLE 3-7. FINAL LAND USE DISTRIBUTION FOR THE LAKE TAHOE BASIN ...................................................37 TABLE 4-1. SUMMARY OF SNOW MODULE CALIBRATION PARAMETERS (ADJUSTED PARAMETERS................. 47 ARE HIGHLIGHTED)........................................................................................................................................ 47 TABLE 4-2. HYDROLOGY VALIDATION SUMMARY STATISTICS FOR WARD CREEK ........................................ 50 TABLE 4-3. SUMMARY OF SEDIMENT LOAD ESTIMATES AT WARD CREEK USING THE M INIMUM ................... 55 VALUE UNBIASED ESTIMATOR...................................................................................................................... 55 TABLE 4-4. SUMMARY OF MONITORING DATA COLLECTED AT THE WARD CREEK OUTLET........................... 56 TABLE 4-5. BASE FLOW AND STORM FLOW SEDIMENT AND NUTRIENT RATING CURVE SUMMARY................. 58 TABLE 4-7. RELATIVE POLLUTANT CONCENTRATIONS FOR MODELED LAND USES (NOTE: APPENDIX B

    DESCRIBES HOW THESE NUMBERS WERE DETERMINED). ...................................................................... 61 TABLE 6-1. MATRIX SUMMARY OF CLIMATE CHANGE SCENARIOS................................................................. 73 TABLE 6-2. DESCRIPTIONS FOR CLIMATE CHANGE SCENARIOS...................................................................... 73 TABLE 6-3. SELECTED MODEL OUTPUT PARAMETERS FOR CLIMATE CHANGE ANALYSIS............................... 75

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    ACRONYMS BF – base flow BMP – best management practice CICU – Commercial/Institutional/Communications/Utilities CTC – California Tahoe Conservancy DEM – Digital Elevation Model DLM – Dynamic Lake Model DON – dissolved organic nitrogen DOP – dissolved organic phosphorus DRI – Desert Research Institute D-Team – TMDL Development Team ERA – Equivalent Roaded Area ET – evapotranspiration GIS – geographic information system GQUAL – general water quality module HIC – hard impervious cover HSPF – Hydrologic Simulation Program–FORTRAN HYSEP – hydrograph separation LRWQCB – Lahontan Region Water Quality Control Board LSPC – Loading Simulation Program C++ LTIMP – Lake Tahoe Interagency Monitoring Program MFR – multi-family residential MVUE – Minimum Variance Unbiased Estimator NCAR – National Center for Atmospheric Research NCDC – National Climatic Data Center NCEP – National Center for Environmental Prediction NDEP – Nevada Department of Environmental Protection NH4 – ammonia NHD – National Hydrography Dataset NO3 – nitrate NRCS – Natural Resources Conservation Service ONRW – Outstanding National Resource Water PEVT – potential evapotranspiration RO – storm flow SFR – single-family residential SNOTEL – SNOpack TELemetry SRP – soluble reactive phosphorus STATSGO – State Soil Geographic database SWE – snow water equivalent TKN – total Kjeldahl nitrogen TMDL – Total Maximum Daily Load TN – total nitrogen TP – total phosphorus TRPA – Tahoe Regional Planning Agency TSS – total suspended sediment Tt – Tetra Tech, Inc. UC Davis – University of California at Davis UCDHRL – Hydrologic Research Laboratory at the University of California at Davis USDA – United States Department of Agriculture USEPA – United States Environmental Protection Agency USGS – United States Geological Survey USLE – Universal Soil Loss Equation

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    1. PROJECT DEFINITION

    Considered a national treasure, and designated by the United States Environmental Protection Agency (USEPA) as an Outstanding National Resource Water (ONRW), beautiful Lake Tahoe and its surrounding watershed have captured the eyes and imaginations of the public and scientists for many decades. Situated high in the Sierra Nevada Mountains across the California–Nevada state border, the Lake Tahoe Basin covers approximately 315 square miles; the lake elevation is at about 6,220 feet (Figure 1-1). The basin is characterized by steep mountain slopes, evergreen and mixed forests, and urban development at various locations around the perimeter of the lake. Popular recreational activities include skiing, hiking, and camping, as well as other outdoor activities. Lake Tahoe is one of the most pristine lakes in the world. In recent decades, however, once-pristine portions of the Lake Tahoe Basin have become urbanized. Studies during the past 40 years have shown that many factors have interacted to degrade the basin’s air quality, terrestrial landscape, and water quality. These factors include land disturbance, an increasing resident and tourist population, habitat destruction, air pollution, soil erosion, roads and road maintenance, and loss of natural landscapes capable of detaining and infiltrating rainfall runoff. Since 1968 the lake’s Secchi depth clarity has declined at a rate of nearly 1 foot per year. To stop and reverse this trend, a Total Maximum Daily Load (TMDL) and associated basin management plan are being developed for the Lake Tahoe Basin. The TMDL process identifies the maximum load of a pollutant a waterbody is able to assimilate while still fully supporting its designated uses. The TMDL process also allocates portions of the allowable load to all sources, identifies the necessary controls that might be implemented voluntarily or through regulatory means, and describes a monitoring plan and associated corrective feedback loop to ensure that uses are fully supported. Watershed modeling is often used during TMDL development to help with one or more of these tasks. Models can be used to help fill in gaps in observed water quality data, estimate existing pollutant sources throughout a watershed, calculate allowable loads, and assess the potential effectiveness of various control options.

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    Figure 1-1. Location of the Lake Tahoe Basin.

    A TMDL for Lake Tahoe is under development; it has an endpoint target of the mean annual water clarity (measured as Secchi depth) during the period 1967–1971. In support of this effort, a comprehensive watershed model has been developed for the Lake Tahoe Basin as part of the 2007 Lake Tahoe technical TMDL initiative (Reuter and Roberts 2004). The primary reasons for developing a watershed model for Lake Tahoe are the following:

    • To determine basin-wide estimates for watershed loading of sediment and nutrients to Lake Tahoe based on land use type

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    • To provide input to the Dynamic Lake Model (DLM) for the Clarity TMDL, developed by the University of California at Davis (Schladow et. al 2004)

    • To create a platform to determine the allowable pollutant load or load allocation from each subwatershed

    • To project load reductions from various best management practices (BMPs) and other management scenarios

    No such model had been previously developed for the Lake Tahoe Basin. The physical setting (which includes a complex topography with 63 individual watersheds plus numerous large parcels that drain directly to the lake), climate patterns, hydrologic/geologic characteristics, and pollutant management considerations demanded an innovative solution and approach for watershed modeling. Integral to the Lake Tahoe modeling effort was adaptation of the model to include scientific results from multiple studies by various research institutions, as well as unique subalpine environment considerations. The high level of detail involved in compiling, analyzing, and organizing the required data for the modeling effort not only benefits the current TMDL objectives but also forms a lasting database of information to support other future scientific and water quality planning studies in the basin. The purpose of this document is to explain the watershed modeling approach and present results for the Lake Tahoe Basin. The model selection process, modeling approach, and model testing or calibration process are detailed. Results of model application to predict existing conditions and alternative loading scenarios are also presented. Detailed results from the watershed model are being used as input data for the DLM.

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    2. MODEL SELECTION

    Two different types of models were necessary to simulate conditions in the Lake Tahoe Basin. A watershed model was used to address the generation of pollutant loads over the land surface and through groundwater contributions, as well as to predict the resulting impact on stream water quality. A separate receiving water model was necessary to simulate conditions in Lake Tahoe itself (Perez-Losada 2001, Reuter and Roberts 2004, Swift 2004). This document focuses on the watershed model.

    A watershed model is essentially a series of algorithms that integrate meteorological forcing data and watershed characteristics to simulate upland and tributary routing processes, including hydrology and pollutant transport. Once a model has been adequately set up and calibrated, and the dominant unit processes are deemed representative on the basis of comparison with available monitored conditions, it becomes a useful tool to quantify existing flows and loads from tributaries without gages and from diffuse overland flow sources. Figure 2-1 illustrates the conceptual data flow for the Lake Tahoe Watershed Model. Such a model provides an interactive system for evaluating “what-if” scenarios associated with management activities.

    Figure 2-1. Conceptual data flow and interactions for a watershed model.

    SubwatershedBoundaries andStream Network

    Land Processes

    Total Upland Flow &

    Load

    Landuse Distribution

    Stream Processes

    Climate Data

    SubwatershedBoundaries andStream Network

    Land Processes

    Total Upland Flow &

    Load

    Landuse Distribution

    Stream Processes

    Climate Data

    SubwatershedBoundaries andStream Network

    Land Processes

    Total Upland Flow &

    Load

    Landuse Distribution

    Stream Processes

    Climate Data

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    Like watershed models, receiving water models are composed of a series of algorithms applied to characteristics data to simulate flow/currents and water quality in a water body. The characteristics data, however, represent physical and chemical aspects of a river, lake, or estuary rather than those of the watershed. These models vary from simple 1-dimensional models to complex 3-dimensional models capable of simulating water movement, salinity, temperature, sediment transport, and water quality. The UC Davis Dynamic Lake Model (DLM), coupled with a water quality sub-model and a newly developed optical sub-model (Swift et al. 2006), was chosen to simulate water quality in Lake Tahoe.

    2.1. Selection Criteria The pollutants of concern for the current modeling application are fine sediment and nutrients, specifically nitrogen and phosphorus. Fine sediment (particles < 63 µm) is represented as a fraction of the total suspended sediment (TSS) observed in the tributaries. Land use in the Lake Tahoe Basin includes extensive areas of largely undeveloped forest and shrub lands, residential areas with sections of high-intensity development, and areas disturbed by forestry operations and fires. Different potential sources of pollutants are associated with each of the various land uses, and each land use affects the hydrology of the basin in a different way. Some of these sources contribute relatively constant discharges of pollutants, whereas others are heavily influenced by snowmelt and rain events. The selection criteria for a specific watershed model were based on technical, regulatory, and stakeholder-specified considerations in the Lake Tahoe Basin. Based on these considerations, the following factors were considered critical to selecting an appropriate watershed model. The model should:

    • Be able to quantify the pollutants of concern (sediment and nutrients) • Be able to address a watershed that has a combination of rural and urban land uses • Be appropriate for simulating a large number of subwatersheds • Provide adequate time-step estimation of flow and not oversimplify storm events

    to provide accurate representation of rainfall events/snowmelt and resulting peak runoff

    • Be capable of simulating various pollutant transport mechanisms (e.g., groundwater contributions and sheet flow)

    • Include an acceptable snowfall and snowmelt routine • Be flexible enough to accommodate issues such as the mountainous environment,

    where topography and meteorological conditions can change within a relatively small distance

    • Be able to be calibrated and validated with the existing long-term data in the database available through the Lake Tahoe Interagency Monitoring Program (LTIMP)

    • Be able to be linked to an appropriate receiving water/lake model

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    • Be a sound platform for evaluating both existing baseline and hypothetical management decisions

    • Be based on best available data and science • Be non-proprietary, tested, and approved by USEPA • Be adaptable and available for future applications

    2.2. Loading Simulation Program C++ (LSPC) Overview On the basis of the considerations described above and previous modeling experience, the USEPA-approved Loading Simulation Program C++ (LSPC) was selected for Lake Tahoe watershed modeling (http://www.epa.gov/athens/wwqtsc/html/lspc.html). LSPC is a watershed modeling system that includes Hydrologic Simulation Program–FORTRAN (HSPF) algorithms for simulating watershed hydrology, erosion, and water quality processes, as well as in-stream transport processes. LSPC integrates a geographic information system (GIS), comprehensive data storage and management capabilities, the original HSPF algorithms, and a data analysis/post-processing system into a convenient PC-based Windows environment. The algorithms of LSPC are identical to a subset of those in the HSPF model. LSPC is maintained by the USEPA Office of Research and Development in Athens, Georgia, and is a component of USEPA’s National TMDL Toolbox (http://www.epa.gov/athens/wwqtsc/index.html). A brief overview of the HSPF model is provided below; a detailed discussion of HSPF-simulated processes and model parameters is available in the HSPF user's manual (Bicknell et al. 1997). HSPF is a comprehensive watershed and receiving water quality modeling framework that was originally developed in the mid-1970s. During the past several years it has been used to develop hundreds of USEPA-approved TMDLs, and it is generally considered the most advanced hydrologic and watershed loading model available. The hydrologic portion of HSPF/LSPC is based on the Stanford Watershed Model (Crawford and Linsley 1966), which was one of the pioneering watershed models. The HSPF framework is developed in a modular fashion with many different components that can be assembled in different ways, depending on the objectives of the individual project. The model includes these major modules:

    • PERLND for simulating watershed processes on pervious land areas • IMPLND for simulating processes on impervious land areas • SEDMNT for simulating production and removal of sediment • RCHRES for simulating processes in streams and vertically mixed lakes • SEDTRN for simulating transport, deposition, and scour of sediment in streams

    All of these modules include many submodules that calculate the various hydrologic, sediment, and water quality processes in the watershed. Many options are available for both simplified and complex process formulations. Spatially, the watershed is divided into a series of subbasins or subwatersheds representing the drainage areas that contribute to each of the stream reaches. These subwatersheds are then further subdivided into segments representing different land uses. For the developed areas, the land use segments

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    are further divided into pervious and impervious fractions. The stream network links the surface runoff and subsurface flow contributions from each of the land segments and subwatersheds and routes them through the water bodies using storage-routing techniques. The stream-routing component considers direct precipitation and evaporation from the water surfaces, as well as flow contributions from the watershed, tributaries, and upstream stream reaches. Flow withdrawals and diversions can also be accommodated. The stream network is constructed to represent all the major tributary streams, as well as different portions of stream reaches where significant changes in water quality occur. Like the watershed components, several options are available for simulating water quality in the receiving waters. The simpler options consider transport through the waterways and represent all transformations and removal processes using simple first-order decay approaches. Decay may be used to represent the net loss due to processes like settling and adsorption. Judging from the relatively high delivery efficiency of the Lake Tahoe tributaries, water quality constituents are likely to remain somewhat conservative. The LSPC framework is flexible and allows different combinations of constituents to be modeled depending on data availability and the objectives of the study. The advantages of choosing LSPC as the watershed model for the Lake Tahoe Basin include the following:

    • It simulates all the necessary constituents and applies to rural and urban watersheds.

    • It has a comprehensive modeling framework that uses the proposed LSPC approach, thereby facilitating development of TMDLs not only for this project but also for potential future projects to address other impairments throughout the Lake Tahoe Basin/

    • It allows for customization of algorithms and subroutines to accommodate the particular needs of the Lake Tahoe Basin.

    • The time-variable nature of the modeling enables a straightforward evaluation of the cause-effect relationship between source contributions and water body response, as well as direct comparison to relevant water quality criteria.

    • The proposed modeling tools are in the public domain and approved by USEPA for use in TMDLs.

    • The model includes both surface runoff and base flow (groundwater) conditions. • It provides storage of all physiographic, point source/withdrawal data and

    process-based modeling parameters in a Microsoft Access database and text file formats to provide for efficient manipulation of data.

    • It presents no inherent limitations with respect to the size and number of watersheds and streams that can be modeled.

    • It provides flexible model output options for efficient post-processing and analysis designed specifically to support TMDL development and reporting requirements.

    • It can be linked to the Lake Tahoe receiving water model (DLM).

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    3. MODELING APPROACH

    This section of the report describes the LSPC modeling approach used for the Lake Tahoe Basin. Developing and applying the LSPC model to address the project objectives involved the following important steps:

    1. Watershed segmentation 2. Water body representation 3. Configuration of key model components––meteorological data, land use

    representation, and soils 4. Model calibration and validation (for hydrology, sediment, and nutrients) 5. Model simulation for existing conditions and scenarios

    The first three steps are discussed in this section of the report. The fourth and fifth steps are discussed in Sections 4 and 5, respectively.

    3.1. Watershed Segmentation LSPC was configured to simulate the entire Lake Tahoe Basin as a series of hydrologically connected subwatersheds. The delineation of subwatersheds was based primarily on topography, but it also considered spatial variation in sources, hydrology, jurisdictional boundaries, and the location of water quality monitoring and stream flow gaging stations. The spatial division of the watersheds allowed for a more refined resolution of pollutant sources and a more representative description of hydrologic variability. Representing elevation change in gradual increments was an important consideration for subwatershed delineation. Because air temperature at a monitoring station is adjusted according to mean watershed elevation during snow simulation (see Section 3.3), subwatershed delineation alone can affect spatially predicted snowfall. The great variation in topography and land uses in the Lake Tahoe Basin required that the subwatersheds be small enough to minimize these averaging effects and to capture the spatial variability. Lake Tahoe’s drainage area was divided into 184 subwatersheds representing 63 direct tributary inputs to the lake. The average size of each subwatershed was 1,100 acres. Areas between stream mouths that directly drain into the lake (intervening zones) were modeled separately. Ten groups of intervening zones were represented in the model. Figure 3-1 shows elevation change and the subwatershed delineation for the watershed model.

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    Figure 3-1. Subwatershed delineation and elevation in the Lake Tahoe Basin.

    3.2. Water Body Representation Each delineated subwatershed in the LSPC model is conceptually represented; a single stream is assumed to be a completely mixed, one-dimensional segment with a constant trapezoidal cross-section (Figure 3-2). The National Hydrography Dataset (NHD) stream reach network was used to determine the representative stream length for each subwatershed. Once the representative reach was identified, slopes were calculated based on Digital Elevation Model (DEM) data and stream lengths were measured from the original NHD stream coverage. Mean depths and channel widths for a number of

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    segments were available from field surveys conducted by the United States Department of Agriculture (USDA)–Agricultural Research Service (Simon et al. 2003). Assuming representative trapezoidal geometry for all streams, mean stream depth and channel width were estimated, using regression curves that relate upstream drainage area to stream dimensions, and were compared with stream surveys at selected locations––General Creek (a wetter west shore of the basin) and Logan House Creek (a drier east shore of the basin). The rating curves consisted of a representative depth-outflow-volume-surface area relationship. An estimated Manning’s roughness coefficient of 0.02 was applied to each representative stream reach based on typical literature values (Schwab et al. 1993).

    Figure 3-2. Stream channel representation in the LSPC model.

    3.3. Meteorological Data Hydrologic processes are time-varying and depend on changes in environmental conditions, including precipitation, temperature, and wind speed. As a result, meteorological data are a critical component of watershed models. Meteorological conditions are the driving force for nonpoint source transport processes in watershed modeling. Generally, the finer the spatial and temporal resolution available for meteorology, the more representative the associated watershed processes will be. As a minimum, precipitation and evapotranspiration are required as forcing functions for most watershed models. For the Lake Tahoe Basin, where the snowfall/snowmelt process is the most significant factor in basin-wide hydrology, additional data were required for snow simulation. These data are temperature, dew point temperature, wind speed, and solar radiation. The physical setting of the basin and the topographic relief cause significantly high variability in weather patterns over a relatively short distance in the same basin. In addition, orographic effects at Lake Tahoe result in a pronounced rain shadow reaching from the much wetter west side to the drier east side. This section discusses local observed weather data used for model calibration; customization of observed data to local influences; and a high-resolution, grid-based synthetic dataset (MM5) originally planned for use during the TMDL scenario runs.

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    Local Weather Data

    An hourly time step for weather data was required to properly reflect diurnal temperature changes. For snow simulation, the model uses temperature to decide whether precipitation should be considered as rainfall or snowfall. Proper prediction of this trigger is required to ensure proper timing of water delivery to the rest of the hydrologic cycle. The timing of rainfall and snowmelt events directly relates to the timing of predicted sediment and nutrient loading. Likewise, the DLM requires proper timing of watershed boundary conditions for predictive accuracy. There were two primary data sources for locally observed weather data. One source was a series of nine Snowpack Telemetry (SNOTEL) gages in and around the Lake Tahoe Basin maintained by USDA’s Natural Resources Conservation Service (NRCS). The SNOTEL sites record air temperature, precipitation, and snow water equivalent data (used for snowfall/snowmelt calibration). The other data source was the National Climatic Data Center (NCDC), which maintains a network of long-term weather stations in the region. South Lake Tahoe Airport was the only hourly surface air gage inside the basin. Table 3-1 lists the weather datasets used to generate the weather forcing files for watershed modeling, and Figure 3-3 shows the location of the SNOTEL and NCDC weather stations in the watershed. Table 3-1. Weather stations and associated data used to simulate weather conditions

    Station Name Code Agency a Data Type b Elevation

    (ft) Available Data

    Echo Peak ECOC1 NRCS SNOTEL 7800 precipitation, temperature Fallen Leaf FLFC1 NRCS SNOTEL 6300 precipitation, temperature Hagan’s Meadow HGNC1 NRCS SNOTEL 8000 precipitation, temperature Heavenly HVNC1 NRCS SNOTEL 8850 precipitation, temperature Marlette MRLN2 NRCS SNOTEL 8000 precipitation, temperature Mount Rose Skic MRSN2 NRCS SNOTEL 8850 precipitation, temperature Rubicon RUBC1 NRCS SNOTEL 7500 precipitation, temperature Tahoe Crossing THOC1 NRCS SNOTEL 6750 precipitation, temperature Ward Creek WRDC1 NRCS SNOTEL 6750 precipitation, temperature South Lake Tahoe AP

    93230 NCDC Hourly 6314 dew point, wind, solar radiation

    Reno APc 23185 NCDC Hourly 4410 dew point, wind, solar radiation Emigrant Gap APc 23225 NCDC Hourly 5276 dew point, wind, solar radiation

    aNRCS is the National Resource Conservation Service; NCDC is the National Climatic Data Center. bSNOTEL indicates data from Snowpack Telemetry stations (available as daily and hourly). cThese weather stations are outside the Lake Tahoe Basin.

  • 12

    Figure 3-3. Location of SNOTEL and NCDC weather stations in the Lake Tahoe Basin.

  • 13

    Local Temperature Data

    Model testing revealed some inconsistencies in the hourly SNOTEL temperature and precipitation observations when first applied directly. These discrepancies needed to be addressed to perform snow and hydrology calibration. As previously described, the snowfall simulation module was especially sensitive to air temperature data because temperature determines whether precipitation is considered as rain or snow. The implications of just a few degrees of error were significant. Missing a single fairly sizable snowfall event could disrupt the entire snowpack dynamics for the year, causing melting when snow accumulation should be occurring. Conversely, if rainfall was incorrectly considered as snow, pack accumulation occurred instead of the expected rain-on-snow response. These inconsistencies became especially evident when snowfall was predicted in July and August of 2000 at the Fallen Leaf station during a model testing run. Consequently, discrepancies in these data were carefully reviewed and corrected. Figure 3-4 shows the corrected SNOTEL temperature time series at Fallen Leaf station.

    Figure 3-4. Original vs. corrected SNOTEL temperature time series at Fallen Leaf Lake.

    Through conversations with NRCS staff regarding the data-reporting procedures, it was learned that daily precipitation totals and minimum/maximum temperatures were more rigorously validated than the hourly datasets. Furthermore, although the SNOTEL dataset included quality flags for impaired values, some of the reportedly unimpaired values were outside the minimum and maximum temperature range. Those values were flagged as impaired. A rigorous quality assurance procedure was developed and applied to consistently process all hourly SNOTEL data from all sites into an acceptable condition for watershed modeling. From Figure 3-4, one can discern gage reporting history, including changes in reporting frequencies, periods of missing or impaired datasets, and

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  • 14

    periods of missing or impaired hourly data. For example, before October 1996 only daily values were recorded. Diurnal disaggregating of NRCS-validated minimum and maximum temperature was used to patch missing or impaired hourly values.

    Lapse Rate Calculations

    Another critical model parameter for snow simulation is the temperature correction for elevation changes (lapse rate). Temperature lapse rate––the rate at which temperature decreases with increasing elevation––significantly influences snowfall prediction, especially when extrapolating snow behavior to subwatersheds without gages. This rate is particularly important in the Lake Tahoe Basin, where elevation changes rapidly with distance from the lake. LSPC estimates lapse rate as a function of the elevation difference between the mean subwatershed elevation and the elevation at the location where temperature is gaged. Figure 3-5 shows scatter plots and linear regression for temperature versus elevation for SNOTEL gages in the basin. The slope of the line is the Tahoe-specific lapse rate approximation, which averages about 0.0022 degrees Fahrenheit (°F) per foot difference in elevation (with an R-squared value of 0.875).

    Figure 3-5. Scatter plots of SNOTEL temperature vs. elevation for regional lapse rate estimate.

    One outlier to the trend was the Echo Peak gage. Although that gage was at a relatively high elevation, it had the highest overall temperature of all the compared gages. At the same time, Echo Peak experiences the second-highest amount of precipitation and snowfall despite its high temperatures. Data analysis showed that snow accumulation frequently occurred even while temperatures approached 40 °F. An explanation for this might be found by examining the areas immediately surrounding the gage. Photographs of the gage show that it is on a crest with very little surrounding vegetation. Another

    SNOTEL Temperature Lapse Rate

    y = -0.0021x + 57.229R2 = 0.5051

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  • 15

    factor that was not considered in this lapse rate adjustment exercise is the local topography surrounding the gage. East-facing versus west-facing slopes might tend to shade the gage or expose it to solar radiation. It is possible that this combination of factors exposes the gage to unimpaired heat from solar radiation. At the same time, the surrounding mountains at Echo Peak are probably responsible for inducing more precipitation. The snowpack most likely persists because it easily reflects solar radiation and the rocky ground beneath remains cold. Consequently, the lapse rate for data excluding Echo Peak was used in LPSC. The watershed model simulates both a wet- and dry-weather lapse rate. HSPF and LSPC assume a default wet lapse rate of 0.0035 °F per foot difference in elevation. The default hourly dry lapse rates vary between 0.0035 and 0.005 °F per foot (Bicknell et al. 1997). Data analysis indicated that actual temperature lapse rates in the Lake Tahoe Basin are probably about 40 to 60 percent lower than the default values used in the model. During snow simulation, a user-defined parameter (ELDAT) is the mean difference between watershed elevation and the temperature gage elevation. The original values were derived from GIS analysis; however, since ELDAT and lapse rate are linearly related, a 40 to 60 percent ELDAT reduction properly corrected for Tahoe-specific conditions.

    Evapotranspiration Calculations

    Following snowfall/snowmelt simulation, evapotranspiration is arguably the second most important factor influencing Lake Tahoe Basin hydrology. Evapotranspiration in the model is used to represent the sum of the evaporation and transpiration that occur due to plants in their natural environment. LSPC requires, as a weather input, the potential evapotranspiration (PEVT), which is the maximum naturally achievable amount of evapotranspiration at any given moment. Model testing revealed that the method selected for computing PEVT in Lake Tahoe was of great significance. Although some methods for actually measuring evapotranspiration in the field are available, most practitioners estimate evapotranspiration using empirical formulations that are a function of other related (and more commonly observed) weather data. Three widely used methods are the Hamon method (1961), the Jensen-Haise method (1963), and the Penman pan-evaporation method (1948). The Penman method, which is the earliest of these three methods, computes evaporation as a function of temperature, solar radiation, dew point or relative humidity, and wind movement. The other two methods, Hamon and Jensen-Haise, are simplified empirical representations that require fewer observed datasets to compute. The Hamon method is a function of only temperature, while the Jensen-Haise method requires solar radiation and temperature. The Penman method (1948) was most suitable for Lake Tahoe. An average vegetation (crop) factor of 0.875 (based on calibration to observed Tahoe City reference evapotranspiration) was used to translate Penman pan-evaporation to PEVT. Figure 3-6 shows monthly modeled evapotranspiration plotted against reference monthly evapotranspiration at Tahoe City. The annual observed evapotranspiration at Tahoe City

  • 16

    is between 35.5 and 42.5 inches per year for reference crop (crop factor of 1.0) and evergreen forest (crop factor of 1.2).

    Figure 3-6. Monthly modeled evapotranspiration (ET) at Ward Creek vs. observed ET at Tahoe City.

    Synthetic Weather Dataset

    As previously mentioned, a synthetic weather dataset (MM5) was developed for TMDL scenario runs. It was not used for model calibration; only actual observed data were used during calibration. The TMDL target for lake clarity is defined as the mean annual Secchi depth during the period 1967–1971. However, with a hydraulic residence time of approximately 650 years, a nutrient doubling time on the scale of a few decades, and paleolimnologic data that show a lake recovery time on the order of many decades (Heyvaert 1998, Jassby et al. 1995), the existing spatial and temporal coverage for meteorological data was not adequate to model future conditions over an appropriate ecological time scale. High-temporal-resolution weather observations for a long period of record are rarely available at a small enough scale to reflect the high degree of spatial climate variability known to exist in the Lake Tahoe Basin. A traditional way of overcoming this difficulty is to statistically interpolate values between existing weather stations where actual observations are available. Although this type of approach works well for a geographically dense monitoring network with fairly homogenous meteorological characteristics, it can prove problematic in a setting like Lake Tahoe, where the network

    * Historical average monthly reference crop evapotranspiration for Tahoe City, California UC Davis Division of Agriculture and Natural Resources, Publication 21454

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    of stations has low spatial density and the physical setting naturally causes high spatial variability in meteorology. There are numerous distinct micro-climate pockets throughout the drainage area. To accomplish the goals of this modeling project, TMDL strategists envisioned using 42 years of reconstructed meteorological input as the basis for extrapolating future conditions, taking the potential influence of climate change into account to the extent possible. To perform this research and development effort, the Lahontan Regional Water Quality Control Board (LRWQCB) contracted with a team from the Hydrologic Research Laboratory at the University of California at Davis (UCDHRL) led by Dr. M. Levent Kavvas. The strategy for the TMDL developers was to use the previous 42 years of weather data to drive watershed modeling into the future (by extrapolating likely weather conditions). The UC Davis research team developed a 42-year history, with 1-hour time steps, of meteorological conditions at a 3- by 3-kilometer square resolution for the entire drainage area, resulting in 142 unique sets of meteorological information. This state-of-the-art meteorological reconstruction process was performed using a regional atmospheric model called MM5 (Anderson et al. 2004). MM5, the fifth-generation atmospheric model developed jointly by the National Center for Atmospheric Research (NCAR) and Pennsylvania State University, is particularly well suited for steep mountainous terrains like the Lake Tahoe Basin (Anderson et al. 2004). The MM5 meteorological data represent a synthetically generated coverage of the basin. Because MM5 is a model, it is an approximation of what might actually be occurring at a particular location. The primary purpose of this information is to support long-term hypothetical modeling scenarios. It is important to note that MM5 calibration was actually performed using real data observations at select locations throughout the basin and at nearby sites outside the basin. While the UC Davis meteorological output included precipitation, surface air temperature, dew point temperature, downward longwave radiation, downward solar radiation, relative humidity, latent heat flux, and wind speed, calibration focused on air temperature and precipitation data from the period 1996–2000 (Anderson et al. 2004). The MM5 output is not suitable for calibrating processes and response within the LSPC watershed model. As previously described, locally observed data from meteorological gages in and around the Lake Tahoe Basin were applied for model calibration. Inputs for the MM5 model included a dataset from the National Center for Environmental Prediction (NCEP), which consisted of 12-hour time interval records from 1958 to 2000 taken over a 285- by 285-kilometer area covering parts of California and Nevada, and orographic information about the region (Anderson et al. 2004). Through extensive computational demand, MM5 scales down the larger/coarser NCEP data to a 3- by 3-kilometer resolution considering orographic changes throughout the modeling area. A significant amount of processing and translation was required to convert the MM5 regional weather predictions into a format suitable for watershed modeling. Five types of

  • 18

    weather information directly extracted from the MM5 output are precipitation, air temperature, dew point temperature, wind speed, and solar radiation. Evapotranspiration, represented as a function of air and dew point temperature, wind movement, and solar radiation, was derived for the entire Lake Tahoe Basin area using the Penman method (Penman 1948). These six different types of weather information predicted at 142 locations resulted in a set of 852 unique hourly time series for driving the watershed model scenarios. Figure 3-7 shows the spatial position of the 142 weather grid cells in relation to the Lake Tahoe watershed area. Because the original MM5 model output was formatted in terms of spatial snapshots reported over time, it was necessary to transpose the entire dataset into temporal profiles at each location in space for the model. After the information at each of the 142 weather grids was processed into the required format for direct linkage to the Lake Tahoe watershed model, data were assigned to each of the 184 subwatersheds using the Theissen polygon method. Because climate was predicted at the grid centroids, and all the grid cells were 3- by 3-kilometer squares uniformly distributed over the drainage area, the Theissen polygon method was equivalent to a straight intersect between the weather grids and the subwatershed boundaries. Weights were assigned to each of the 142 grid cells and aggregated to a subwatershed basis using the area fractions of grid cells intersecting each subwatershed boundary. This approach provided a very high degree of spatial and time resolution not typically seen in watershed modeling.

  • 19

    Figure 3-7. Location of the 142 MM5 weather grid cells in the Lake Tahoe Basin. During MM5 model development, the model was guided by data from several gages spanning a wide area in and around outside the Lake Tahoe Basin (Anderson et al., 2004). To gage the predictive ability of the MM5 meteorology to drive the Lake Tahoe watershed model, further validation of long-term MM5 summaries against observed SNOTEL summaries was performed. There were nine SNOTEL gages within the domain of the MM5 spatial grid coverage. Data from the nearest SNOTEL station were compared with the synthetic data at the nearest MM5 grid with similar elevation to assess predictive comparability throughout the basin. Figure 3-8 shows the location of the SNOTEL gages relative to selected MM5 cells with comparable elevation. Table 3-2 contains additional information about the nine SNOTEL gages.

  • 20

    Figure 3-8. Location of SNOTEL gages relative to selected MM5 cells with comparable elevation.

  • 21

    Table 3-2. SNOTEL gages and summary information (October 1990- September 2000).

    Station Name Code Elevation

    (m) Precipitation

    (in/yr) Temperature

    (Deg C) Echo Peak ECOC1 2,377 62 7.7 Fallen Leaf FLFC1 1,920 37 6.2 Hagens Meadow HGNC1 2,438 34 4.3 Heavenly HVNC1 2,698 41 3.2 Marlette MRLN2 2,438 43 4.4 Mount Rose Ski MRSN2 2,698 61 3.4 Rubicon RUBC1 2,286 44 5.4 Tahoe Crossing THOC1 2,057 37 6.6 Ward Creek WRDC1 2,057 71 5.6

    Figure 3-9 shows both modified MM5 versus observed SNOTEL gage elevation and annual average temperature graphs. The Fallen Leaf and Echo Peak SNOTEL data showed temperature trend deviations from what was predicted at the other seven gages. When Fallen Leaf and Echo Peak pairs are excluded, there is very good agreement between long-term MM5 and SNOTEL temperature. The MM5 versus observed data summaries span January 1990 through December 2000.

    Figure 3-9. MM5 vs. observed SNOTEL elevation and temperature. Figure 3-10 further shows comparisons between observed SNOTEL and MM5 temperature predictions. The observed temperature monitored at Echo Peak (ECOC1) is higher than what might be expected to occur at its relatively high elevation; and although the Fallen Leaf (FLFC1) SNOTEL gage is at the lowest elevation in the basin, there might be a slight cooling effect because the gage is situated between two water bodies (Fallen Leaf Lake and Lake Tahoe itself). This discrepancy might propagate error into predicted watershed response for the associated region of the Upper Truckee watershed. Table 3-3 presents the percentage of difference between SNOTEL and MM5 temperatures for the winter season.

    MM5 Grid vs. SNOTEL Gage Elevation

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    Figure 3-10. Predicted MM5 temperature vs. observed SNOTEL temperature and elevation. Table 3-3. Average percentage of difference between SNOTEL and MM5 temperatures during winter season

    Weather Stations Date Ward

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    1/1990–4/1990 No data No data 2% 4% No data -15% 11/1990–4/1991 -9% -1% 1% 7% 18% -15% 11/1991–4/1992 1% 2% 3% 9% 18% -7% 11/1992–4/1993 -4% 3% 0% 7% 16% -7% 11/1993–4/1994 -1% 2% 3% 7% 18% -8% 11/1994–4/1995 -1% 4% 6% 8% 17% -6% 11/1995–4/1996 -3% 1% 1% 7% 15% -7% 11/1996–4/1997 -2% 1% 3% 5% 13% -7% 11/1997–4/1998 -5% 2% 2% 4% 14% -8% 11/1998–4/1999 -2% 3% 6% 5% 17% -7% 11/1999–4/2000 -2% 2% 0% 2% 15% -7% 11/2000–4/2000 8% 0% -4% 13% 22% No data

    The MM5 precipitation prediction is consistently lower than the observed SNOTEL- reported precipitation, although the relative spatial variation approaches the observed trends. Figure 3-11 shows predicted MM5 precipitation, observed SNOTEL precipitation, and SNOTEL gage elevations. Figure 3-12 illustrates seasonal precipitation patterns at Ward Creek for the 10 years between October 1990 and September 2000. The same trend is observed at other MM5 grid cells around the basin. The composite seasonal comparison reveals that the under-predicting months of the year coincide with snowfall- dominated months. One potential limitation of the MM5 predictions is a reduced predictive ability to represent snowfall volumes during fall, winter, and spring. Summer rainfall predictions by MM5 are relatively close in magnitude compared with observed SNOTEL totals. Table 3-4 shows the percentage of difference between SNOTEL and MM5 total precipitation for the winter season.

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    Figure 3-12. Seasonal MM5 precipitation vs. observed SNOTEL precipitation at Ward Creek.

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    Table 3-4. Yearly percentage of difference between SNOTEL and MM5 total precipitation during winter season

    Weather Stations Date Ward

    Creek Rubicon

    #2 Marlette

    Lake Hagans Meadow Fallen Leaf Echo Peak

    1/1990–4/1990 -55% -50% -56% -29% -45% -67% 11/1990–4/1991 -53% -44% -54% -20% -31% -67% 11/1991–4/1992 -46% -35% -48% -31% -42% -70% 11/1992–4/1993 -59% -54% -67% -36% -38% -67% 11/1993–4/1994 -62% -51% -58% -45% -37% -69% 11/1994–4/1995 -60% -64% -61% -11% -46% -62% 11/1995–4/1996 -62% -69% -78% -59% -67% -72% 11/1996–4/1997 -67% -63% -56% -38% -44% -69% 11/1997–4/1998 -56% -47% -64% -31% -49% -62% 11/1998–4/1999 -58% -57% -71% -42% -63% -70% 11/1999–4/2000 -57% -50% -66% -23% 30% -57% 11/2000–12/2000 -77% -77% -83% -73% -69% -76% The snowfall module includes a parameter called SNOWCF, which accounts for water volume losses due to poor snow catch efficiency at the gages. Although SNOWCF can be adjusted to achieve satisfactory agreement for long-term water volumes, the general timing of the snowpack buildup does not resemble the general shape of observed snowpack buildup. Further refinement of the precipitation predictions might be required to better represent the nature of snowpack buildup. Overall, although the MM5 data represented spatial variation throughout the basin very well, it tended to under-predict precipitation between October and May. The MM5 model developers stated that snow recognition is a limitation of the model. One proposed solution for resolving this difference is to generate and apply spatially derived monthly snow correction between MM5 and observed SNOTEL predictions. Keep in mind that the primary purpose of the MM5 data is to support long-term hypothetical modeling scenarios. The MM5 output is not suitable for calibrating processes and response within the LSPC watershed model, and therefore it was not used for calibration. As previously explained, locally observed data from meteorological gages in and around the Lake Tahoe Basin were applied for model calibration. The model has been successfully calibrated using observed meteorology from the SNOTEL sites. Further refinement of MM5 is required to apply it for running 40-year hypothetical model scenarios; however, no such refinement has been made at this point in time.

    3.4. Land Use Representation LSPC requires a basis for distributing hydrologic and pollutant loading parameters. Such a basis is necessary to appropriately represent hydrologic variability throughout the basin, which is influenced by land surface and subsurface characteristics. It is also necessary to represent variability in pollutant loading, which is highly related to land practices.

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    Land use typically represents the primary unit for computing water quantity and quality. Rural and urban land use areas in individual subwatersheds each contribute runoff containing pollutant loads to a stream that flows to the lake. Lands adjacent to the lake contribute pollutants directly to it. Land use categories were defined in the watershed model for the purpose of evaluating pollutant loading from the Lake Tahoe Basin. The total area of each land use category in each subwatershed was computed and amounts of pollutants generated by land use categories were calculated based on characteristics like soil type, slope, and vegetation. In addition to the need for land use data in computing water quantity and quality, nonpoint source management decisions are also frequently based on land use-related activity at the subwatershed level. Therefore, it was important to have a detailed land use representation with classifications that were meaningful for load allocation and load reduction. For the Lake Tahoe Basin, no single GIS data source was available that could adequately represent land use variability and impacts by itself to a degree high enough to support a detailed water quality modeling effort. Therefore, it was determined that the best approach would be to build a composite layer that included the best aspects of all available components. Developing the Lake Tahoe land use layer required a major effort relying on significant input from several local experts and agencies responsible for land management around the basin. A TMDL Development Team (D-Team) was formed. The D-Team included key staff from the LRWQCB, Nevada Department of Environmental Protection (NDEP), USDA Forest Service Lake Tahoe Basin Management Unit, Desert Research Institute (DRI), the Tahoe Regional Planning Agency (TRPA), California Tahoe Conservancy (CTC), UC Davis, and Tetra Tech, Inc. (Tt). The D-Team located and compiled the most current and representative GIS land use coverage layers available, identified advantages and limitations inherent in each data source, and produced a composite layer that maximized the overall accuracy for representing land use throughout the Lake Tahoe Basin. Figure 3-13 presents the final composite land use coverage.

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    Figure 3-13. Final composite land use coverage for the Lake Tahoe Basin. From a large set of GIS layers that varied in resolution and quality, a plan of action evolved through the data review process. A number of the most critical GIS layers became available only after this project had already begun. The D-Team had to determine a manageable and representative set of land use categories and identify relevant spatial information available for representing each category. Over the course of the development process, certain categories and layers were included or excluded on the basis of ground-truth comparisons, data duplication/exclusion, and site-specific information about the significance of the impact. For example, the initial list of land uses was modified to exclude grazing (a practice that has almost disappeared from the basin and whose historical or legacy impacts are not significant for water quality) and to further refine the

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    open space recreational category into turfed and non-turfed vegetated areas (e.g., golf-courses versus campgrounds). The final land use layer was based on three primary sources of spatial data: (1) an updated parcel boundaries layer from a number of agencies that compose the Tahoe Basin GIS User’s Group, (2) a detailed 1-square-meter-resolution Hard Impervious Cover (HIC) layer that was developed using remote sensing techniques from IKONOS satellite imagery (Minor and Cablk 2004), and (3) a map of upland erosion potential developed by Andrew Simon (Simon et al. 2003).

    The Parcel Boundaries Layer

    A number of agencies composing the Tahoe Basin GIS User’s Group funded the acquisition of the updated parcel boundaries layer. This layer is a highly detailed GIS coverage that all stakeholders can use for a variety of planning purposes. The new coverage was greatly needed because the older parcel layers had been developed using the best available technology and resources at the time, both of which have been significantly improved in recent years. The fundamental advantage of the new parcel layer was the high resolution with which the individual parcels were delineated, classified, and ground-truthed. This new parcel coverage, accurate to within 10 feet (from TRPA correspondence), was used to develop a basin-wide land ownership coverage for TRPA.

    Hard Impervious Cover Layer

    Developed by DRI using spectral mapping and transformation techniques on IKONOS satellite images from 2002 (Minor and Cablk 2004), the HIC layer is a 1-meter-resolution grid map of all anthropogenic impervious surfaces throughout the basin. This high-resolution layer allows for a detailed spatial accounting of impervious surfaces in the basin, including rooftops and paved roads in both urbanized and rural or vegetated areas. Because the degree of directly connected imperviousness significantly affects runoff volume, timing, and pollutant load, it is desirable to accurately represent imperviousness at the parcel scale over the entire basin area. Figure 3-14 shows the hard impervious cover in the Lake Tahoe Basin and an example focus area.

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    Figure 3-14. Hard impervious cover for the Lake Tahoe Basin and example focus area.

    Upland Erosion Potential

    During model development it became evident that the land use category classified as vegetated-unimpacted was too broad and did not reflect significant differences in the erodibility of the soils. Further definition of this category became necessary for successful model calibration. Using the GIS coverage Upland-Erosion Potential for the Lake Tahoe Basin developed by Simon et al. (2003), the land uses previously categorized as Vegetated-Unimpacted were subdivided into five erosion potential categories. A more detailed description of the modeled land uses is included in the following section.

    Land Use Categorization/Reclassification

    It was neither practical nor possible to gather enough hydrology and pollutant loading information to represent each of the 140 land use classifications for 60,000 individual parcel polygons. Furthermore, certain potential disturbance areas could not be directly mapped from the parcel boundaries alone. The D-Team determined the land use categories based on collective agreement from the various agencies involved as to areas with relatively similar response from a water quality modeling perspective and areas for which local or national pollutant runoff reference information could support model

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    representation. The 140 original land use types indicated by the parcel boundary codes were reclassified into the following six general land use categories:

    • Single-family residential (SFR) • Multi-family residential (MFR) • Commercial/Institutional/Communications/Utilities (CICU) • Transportation • Vegetated • Water body

    The D-Team recognized that vegetated (non-urbanized) areas deserved special attention because they constitute over 80 percent of the basin area. Furthermore, the general vegetated lands category included a number of different land uses (e.g., ski resorts and other recreational areas), management activities (e.g., harvesting to control overgrowth and fire hazard), and/or natural conditions (e.g., naturally burned forests) that have differing hydrologic and sediment and nutrient loading characteristics. As a result, six subcategories of vegetated land use were initially defined as follows:

    1. Unimpacted: Forested areas that have been minimally affected in the recent past 2. Turf: Land use types with large turf areas and little impervious coverage, such as

    golf courses, large playing fields, and cemeteries, with potentially similar land management activities

    3. Recreational: Lands that are primarily vegetated and are characterized by relatively low-intensity uses and small amounts of impervious coverage; these include the unpaved portions of campgrounds, visitor centers, and day use areas

    4. Ski Areas: Lands within otherwise vegetated areas for which some trees have been cleared to create a run

    5. Burned: Areas that have been subject to controlled burns and/or wildfires in the recent past

    6. Harvested: Lands that management agencies have thinned in the recent past for the purpose of forest health and defensible space (areas cleared to reduce the spread of wildfire)

    Once the D-Team had agreed on the classifications, team members identified and categorized each parcel on the basis of their agencies’ activities and knowledge of the Lake Tahoe Basin. Selected refinements to the parcel boundary layer were performed to include known areas of disturbance in the basin that had not been identified in the available GIS layers. These areas were ground-truthed and hand-delineated by GIS technicians from the Forest Service, CTC, and NDEP. Through this process, the D-Team identified a complication: the parcel boundary layer often represented ownership jurisdiction better than the actual land use occurring within the selected properties. Therefore, some modifications were required to translate legal or jurisdictional boundaries into actual land uses. Ski areas, campgrounds, parking areas, and primary and secondary roads were all modified. Because the impact from ski areas stems from the disturbance (clearing) of steep slopes, a

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    new GIS category and layer for Ski Runs was developed and used as a refinement for the ski area boundaries previously identified. Land within ski area boundaries that was otherwise fully vegetated and relatively unimpacted was added to the Vegetated Unimpacted land use category, which was collectively refined into five erosion potential categories as described in a following section. Figure 3-15 shows an example of the resulting refinements to the previously defined Vegetated Ski Areas category.

    Figure 3-15. Example of parcel refinements in a portion of the Heavenly Ski Area.

    Campgrounds were hand-delineated based on Forest Service guidance that camping activity typically occurs within 80 feet of roads inside camping areas such as California and Nevada state parks and Forest Service campgrounds. Members of the D-Team obtained supplemental site-specific information from campground brochures and visual confirmation through visits to selected locations. The refined campgrounds were added to the Vegetated-Recreational subcategory. Figure 3-16 illustrates an example of this refinement for campgrounds near Emerald Bay on the southwestern shore of Lake Tahoe.

    Parking lot: Commercial Impervious

    Ski Runs

    Trails in Ski Areas

    Vegetated areas within Ski Areas

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    Figure 3-16. Example of parcel refinement in a campground parcel boundary near Emerald Bay on the southwestern shore of Lake Tahoe. Parking areas in high-traffic recreational facilities, beach areas, and ski resorts were hand-delineated and classified as Commercial or Institutional because of the intensity of usage. Figure 3-15, which shows the Heavenly Ski Area, illustrates the result of this type of refinement. Primary and secondary roads contained in the TRPA parcel coverage delineate the jurisdictional right-of-way, a much wider area than that occupied by the paved road surface. These categories were more accurately represented using the IKONOS HIC layer, by means of a GIS layering and intersecting process (which is described in more detail in the following section, GIS Layering Process). Figure 3-17 illustrates this refinement at the US Route 50 and Route 28 intersection south of Spooner Lake in Nevada.

    Original

    campground parcel

    boundary

    Revised campground

    land use boundary

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    Figure 3-17. Example of parcel refinement for highway right-of-way ownership (image on left) to actual highway widths based on hard-cover impervious overlay (image on right). Supporting GIS layers included Forest Service roads and trails, recreational areas (ski runs and campgrounds), water bodies, and boundaries and dates for forest fires/prescribed burns and harvesting activities. These latter two subcategories were not explicitly represented in the composite layer because they represent episodic impacts. Harvested forest and burned areas were accounted for based on location and calibration time. The GIS Layering Process section below describes how the HIC coverage and fire and timber harvest maps were included in the composite land use coverage for the Lake Tahoe Basin.

    GIS Layering Process

    To produce the land use grid that forms the framework for the LSPC watershed model, a layering and intersecting process for the various land use GIS data sources in the Tahoe Basin was performed. The objective of this effort was to develop one composite grid layer that maximized the overall accuracy in representing land use areas in the Lake Tahoe Basin. Table 3-5 shows the final modeling land use categories derived from the composite land use layer.

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    Table 3-5. Modeling land use categories derived from the composite land use layer

    Land Use Description Pervious/Impervious Subcategory Name Number

    Water body Impervious Water_Body 1 Pervious Residential_SFP 2

    Single-family residential Impervious Residential_SFI 3 Pervious Residential_MFP 4

    Multi-family residential Impervious Residential_MFI 5 Pervious CICU-Pervious 6 Commercial/institutional/

    communications/utilities Impervious CICU-Impervious 7 Impervious Roads_Primary 8 Impervious Roads_Secondary 9 Transportation Impervious Roads_Unpaved 10 Pervious Ski_Areas-Pervious 11 Pervious Veg_Unimpacteda 12 Pervious Veg_Recreational 13 Pervious Veg_Burned 14 Pervious Veg_Harvest 15

    Vegetated

    Pervious Veg_Turf 16 aThis subcategory was further refined into five new subcategories based on erosion potential. GIS layering was performed after all required corrections and refinements to individual parcels had been performed for the entire Basin. Before application of the HIC land use and forest and timber harvesting regions in the GIS layering process, only the categories listed as Pervious in Table 3-5 (excluding Harvested and Burned Vegetated lands) were included in the land use GIS coverage. The incorporation of the separate HIC layer and forest and timber harvest GIS coverages, as well as erosion potential for vegetated areas, is explained below.

    Incorporating the HIC Layer

    Based on visual and tabular/quantitative comparisons of transportation areas as represented in the TRPA land use layer, it was determined that the HIC layer represented road surfaces better than buffering existing road widths with average width information. Therefore, the HIC layer was combined with the TRPA land use layer as described below. First, all existing fields associated with transportation in the TRPA layer were essentially turned off (temporarily) by converting them to Vegetated-Unimpacted. The entire TRPA land use layer was then converted into a 1-meter grid so that it would be compatible with the HIC grid resolution. Doing so made it possible to intersect these two grids, resulting in a unique determination of pervious and impervious grid cells for each land use type. Impervious road surfaces became a fictitious Vegetated-Impervious surface, which could at that point be reclassified as roads. The transportation category was further subdivided into Primary Roads, Secondary Roads, and Unpaved Roads. The first two subcategories are paved surfaces and are

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    represented in the HIC grid. Before merging the HIC grid to the parcel boundary grid, it was necessary to distinguish Primary Road grid cells from other impervious grid cells. To achieve this, a separate highway roads line-theme layer was first uniformly buffered to a width wide enough to span the width of any HIC highway segment (60 feet) and converted into a grid. The new highway grid was intersected with the HIC grid to create Primary Roads HIC grids and Other HIC grids. After isolating Primary Roads HIC grid cells from Other HIC grids, the HIC grid was intersected with the parcel boundary grid. This process was done to distinguish pervious and impervious SFR, MFR, and CICU land use types. Resulting by-products of this merge were a few Vegetated-Impervious cells. Because the right-of-way-influenced transportation categories in the TRPA land use layer were converted to Vegetated before the merge, and because the Primary Roads were already distinguished within the HIC grid, the process of elimination meant that the resulting Vegetated-Impervious land areas would largely represent the remaining Secondary Roads. A few small structures and objects on vegetated land were also discernible, however, because there were very few of these occurrences, they were still included in the Secondary Roads category. The final layer incorporated into the composite land use was Unpaved Roads. Because none of the previously added layers had included unpaved road surfaces (the HIC layer considered only hard-impervious areas like pavement and structures), this merge was the most straightforward. The Unpaved Roads layer was created by buffering the unpaved Forest Service and California and Nevada state park roads by each segment’s specified width from metadata, and merging in recreational trails that were buffered to a 2-foot width (based on basin-wide average trail width). The buffered Unpaved Roads layer was converted to a grid and intersected with the HIC and parcel boundary composite. All the cells intersected by the unpaved roads layer were directly converted to represent Unpaved Roads.

    Incorporating Forest Fire and Harvest Areas

    The remaining vegetated disturbance categories that were not explicitly represented in the TRPA land use coverage included burned and harvested vegetated land and vegetated urban lots. The Forest Service and CTC compiled map layers for fire and timber harvest regions for different events over time. These map layers also represented the degree of burning and harvesting in each affected area. For each burned or harvested zone, an Equivalent Roaded Area (ERA) was computed. The ERA represented the percentage of land in a particular area that was affected by that activity. For example, a harvest ERA of 0.1 indicated that 10 percent of the area within the associated boundary was disturbed due to timber harvesting. Figure 3-18 shows the Gondola Fire, which was a significant forest fire that occurred in 2002 near Heavenly Ski Resort. Subwatershed boundaries are also shown to depict how ERAs were computed at the subwatershed level.

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    Figure 3-18. Forest fire boundaries shaded with burn severity for the Gondola Fire. (The right panel shows how the affected areas are aggregated by subwatershed.)

    Incorporating Erosion Potential for Vegetated Areas

    The land use category classified as Vegetated-Unimpacted was too broad to reflect significant differences in the erodibility of the soils. Therefore, further definition of this category was necessary. The GIS coverage of Upland-Erosion Potential for the Lake Tahoe Basin developed by Simon et. al (2003) (Figure 3-19) was used to subdivide the land uses previously categorized as Vegetated-Unimpacted into five erosion potential categories. The scale, which goes from a low of 1 to a high of 5, refers to the erosion potential ability of the soil: the higher the value, the higher the erosion potential. The map of upland-erosion potential for the Lake Tahoe Basin was developed using an upland-erosion-potential index based on the following parameters:

    • Soil erodibility factor (K factor) • Land use • Paved and unpaved roads, trails and streams • Surficial geology • Slope steepness

    Each land segment was assigned a representative value for each of the previously listed parameters. Finally, the values of each of the five selected parameters were added and reclassified at a scale of 1 to 5.

    Shades indicate degree of burn severity (ERA)

    Burned Areas Burned Areas

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    Figure 3-19. Map of upland erosion potential for the Lake Tahoe Basin. The map of upland erosion potential was used to subdivide the broad vegetated-unimpacted category into five vegetated land use categories: Veg_EP1, Veg_EP2, Veg_EP3, Veg_EP4, and Veg_EP5. Table 3-6 shows the resulting breakdown of coverage in the Tahoe Basin for the 5 categories.

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    Table 3-6. Percent coverage for each of the five Vegetated-Unimpacted categories (based on erosion potential)

    Vegetated Land Use Percent Cover

    Veg_EP1 5.72%

    Veg_EP2 46.28%

    Veg_EP3 26.14%

    Veg_EP4 8.88%

    Veg_EP5 0.22%

    Total 87.02% Finally, Table 3-7 shows the final land use distribution for the Lake Tahoe Basin in descending order of percent area. Table 3-7. Final land use distribution for the Lake Tahoe Basin

    Land Use Percentage of Watershed Area Land Use Percentage of

    Watershed Area Veg_EP2 46.28% Veg_Turf 0.55% Veg_EP3 26.14% Ski_Runs 0.54% Veg_EP4 8.88% CICU-Impervious 0.48% Veg_EP1 5.72% Residential_MFI 0.38%

    Residential_SFP 4.00% Roads_Primary 0.28% Water_Body 1.70% Veg_EP5 0.22%

    Roads_Secondary 1.34% Veg_Burned 0.20% Residential_MFP 1.00% Veg_Harvest 0.20% Residential_SFI 0.89% Veg_Recreational 0.17% CICU-Pervious 0.86% Roads_Unpaved 0.15%

    Once the erosion potential was incorporated into the land use coverage, the composite land use coverage was complete and ready to be used in the LSPC model (Figure 3-13).

    3.5. Soils Soils data and GIS coverages from the 2004 NRCS Soil Survey were originally used to characterize soils in the Lake Tahoe Basin. General soils data and map unit delineations for the United States are provided as part of the State Soil Geographic (STATSGO) database. As of January 2007, a more detailed NRCS Soil Survey Geographic (SSURGO) database has been completed. The following discussion has been revised based on the updated SSURGO database, which will be considered for any potential future model updates. A map unit is composed of several soil series having similar properties. Identification fields in the GIS coverages can be linked to the database that provides information on chemical and physical soil characteristics. Figure 3-20 shows the general map units in the Lake Tahoe Basin, and the following paragraphs summarize relevant soils data.

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    Figure 3-20. SSURGO map units and corresponding soil descriptions. Permeability is defined as the rate at which water moves through soil. It is measured in centimeters per second and varies with soil texture, structure, and pore sizes. Soil uses, such as agriculture, septic systems, and construction, can be limited when permeability is too slow. Clays are usually the least permeable soils and sands and gravels the most permeable. NRCS has provided the minimum and maximum ranges for permeability in the Lake Tahoe Basin in the SSURGO database. For the purpose of this analysis, permeabilities are shown as average values for the entire soil layer of each SSURGO map

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    unit present in the Lake Tahoe Basin. Figure 3-21 shows that permeability in the Lake Tahoe Basin ranges from a moderate 0.42 cm/s to a very rapid 44 cm/s. The soils with the lowest permeabilities are in the northwest quadrant of the basin. A commonly used soil attribute is the K-factor, which is a component of the Universal Soil Loss Equation, or USLE (Wischmeier and Smith 1978). The K-factor is a dimensionless measure of a soil’s natural susceptibility to erosion, and factor val